[FIXED] assign 0 when value_count() is not found

Issue

I have a column that looks like this:

group
A
A
A
B
B
C

The value C exists sometimes but not always. This works fine when the C is present. However, if C does not occur in the column, it throws a key error.

    value_counts = df.group.value_counts()
    new_df["C"] = value_counts.C

I want to check whether C has a count or not. If not, I want to assign new_df["C"] a value of 0. I tried this but i still get a keyerror. What else can I try?

    value_counts = df.group.value_counts()
    new_df["C"] = value_counts.C
    if (df.group.value_counts()['consents']):
      new_df["C"] = value_counts.consents
    else:
      new_df["C"] = 0

Solution

One way of doing it is by converting series into dictionary and getting the key, unless not found return the default value (in your case it is 0):

df = pd.DataFrame({'group': ['A', 'A', 'B', 'B', 'D']})

new_df = {}

character = "C"
new_df[character] = df.group.value_counts().to_dict().get(character, 0)

output of new_df

{'C': 0}

However, I am not sure what new_df should be, it seems that it is a dictionary? Or it might be a new dataframe object?

Answered By - Galilej25